IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-37111-w.html
   My bibliography  Save this article

Spatial transcriptomics using multiplexed deterministic barcoding in tissue

Author

Listed:
  • Johannes Wirth

    (Helmholtz Pioneer Campus, Helmholtz Munich)

  • Nina Huber

    (Helmholtz Pioneer Campus, Helmholtz Munich)

  • Kelvin Yin

    (Helmholtz Pioneer Campus, Helmholtz Munich)

  • Sophie Brood

    (Helmholtz Pioneer Campus, Helmholtz Munich)

  • Simon Chang

    (Helmholtz Pioneer Campus, Helmholtz Munich)

  • Celia P. Martinez-Jimenez

    (Helmholtz Pioneer Campus, Helmholtz Munich
    Technical University of Munich)

  • Matthias Meier

    (Helmholtz Pioneer Campus, Helmholtz Munich
    University of Leipzig)

Abstract

Spatially resolved transcriptomics of tissue sections enables advances in fundamental and applied biomedical research. Here, we present Multiplexed Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm2 with a 50 µm resolution. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared to previous reports. Furthermore, the introduction of alignment markers allowed seamless registration of images and spatial transcriptomic spots. Together with technological advances, we provide an open-source computational pipeline to prepare raw sequencing data for downstream analysis. The functionality of xDBiT was demonstrated by acquiring 16 spatially resolved transcriptomic datasets from five different murine organs, including the cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of spatial transcriptomes allowed for in-depth characterization of the murine kidney.

Suggested Citation

  • Johannes Wirth & Nina Huber & Kelvin Yin & Sophie Brood & Simon Chang & Celia P. Martinez-Jimenez & Matthias Meier, 2023. "Spatial transcriptomics using multiplexed deterministic barcoding in tissue," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37111-w
    DOI: 10.1038/s41467-023-37111-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-37111-w
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-37111-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Madhav Mantri & Gaetano J. Scuderi & Roozbeh Abedini-Nassab & Michael F. Z. Wang & David McKellar & Hao Shi & Benjamin Grodner & Jonathan T. Butcher & Iwijn De Vlaminck, 2021. "Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Seitaro Nomura & Masahiro Satoh & Takanori Fujita & Tomoaki Higo & Tomokazu Sumida & Toshiyuki Ko & Toshihiro Yamaguchi & Takashige Tobita & Atsuhiko T. Naito & Masamichi Ito & Kanna Fujita & Mutsuo H, 2018. "Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure," Nature Communications, Nature, vol. 9(1), pages 1-17, December.
    3. Chee-Huat Linus Eng & Michael Lawson & Qian Zhu & Ruben Dries & Noushin Koulena & Yodai Takei & Jina Yun & Christopher Cronin & Christoph Karp & Guo-Cheng Yuan & Long Cai, 2019. "Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+," Nature, Nature, vol. 568(7751), pages 235-239, April.
    4. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    2. Qingnan Liang & Yuefan Huang & Shan He & Ken Chen, 2023. "Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Yuchen Liang & Guowei Shi & Runlin Cai & Yuchen Yuan & Ziying Xie & Long Yu & Yingjian Huang & Qian Shi & Lizhe Wang & Jun Li & Zhonghui Tang, 2024. "PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Rongbo Shen & Lin Liu & Zihan Wu & Ying Zhang & Zhiyuan Yuan & Junfu Guo & Fan Yang & Chao Zhang & Bichao Chen & Wanwan Feng & Chao Liu & Jing Guo & Guozhen Fan & Yong Zhang & Yuxiang Li & Xun Xu & Ji, 2022. "Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    5. Kian Kalhor & Chien-Ju Chen & Ho Suk Lee & Matthew Cai & Mahsa Nafisi & Richard Que & Carter R. Palmer & Yixu Yuan & Yida Zhang & Xuwen Li & Jinghui Song & Amanda Knoten & Blue B. Lake & Joseph P. Gau, 2024. "Mapping human tissues with highly multiplexed RNA in situ hybridization," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    6. Yahui Long & Kok Siong Ang & Mengwei Li & Kian Long Kelvin Chong & Raman Sethi & Chengwei Zhong & Hang Xu & Zhiwei Ong & Karishma Sachaphibulkij & Ao Chen & Li Zeng & Huazhu Fu & Min Wu & Lina Hsiu Ki, 2023. "Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    7. Juexin Wang & Jinpu Li & Skyler T. Kramer & Li Su & Yuzhou Chang & Chunhui Xu & Michael T. Eadon & Krzysztof Kiryluk & Qin Ma & Dong Xu, 2023. "Dimension-agnostic and granularity-based spatially variable gene identification using BSP," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    8. Lukas M. Weber & Arkajyoti Saha & Abhirup Datta & Kasper D. Hansen & Stephanie C. Hicks, 2023. "nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    9. Xinrui Zhou & Wan Yi Seow & Norbert Ha & Teh How Cheng & Lingfan Jiang & Jeeranan Boonruangkan & Jolene Jie Lin Goh & Shyam Prabhakar & Nigel Chou & Kok Hao Chen, 2024. "Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    10. Wei Liu & Xu Liao & Ziye Luo & Yi Yang & Mai Chan Lau & Yuling Jiao & Xingjie Shi & Weiwei Zhai & Hongkai Ji & Joe Yeong & Jin Liu, 2023. "Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    11. Honglei Ren & Benjamin L. Walker & Zixuan Cang & Qing Nie, 2022. "Identifying multicellular spatiotemporal organization of cells with SpaceFlow," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    12. Zhiyuan Yuan, 2024. "MENDER: fast and scalable tissue structure identification in spatial omics data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    13. S. Vickovic & B. Lötstedt & J. Klughammer & S. Mages & Å Segerstolpe & O. Rozenblatt-Rosen & A. Regev, 2022. "SM-Omics is an automated platform for high-throughput spatial multi-omics," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    14. Aditi Sahu & Kivanc Kose & Lukas Kraehenbuehl & Candice Byers & Aliya Holland & Teguru Tembo & Anthony Santella & Anabel Alfonso & Madison Li & Miguel Cordova & Melissa Gill & Christi Fox & Salvador G, 2022. "In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    15. Zixiang Zhou & Yunshan Zhong & Zemin Zhang & Xianwen Ren, 2023. "Spatial transcriptomics deconvolution at single-cell resolution using Redeconve," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    16. Hua Zhang & Yuan Liu & Lauren Fields & Xudong Shi & Penghsuan Huang & Haiyan Lu & Andrew J. Schneider & Xindi Tang & Luigi Puglielli & Nathan V. Welham & Lingjun Li, 2023. "Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    17. Reza Mirzazadeh & Zaneta Andrusivova & Ludvig Larsson & Phillip T. Newton & Leire Alonso Galicia & Xesús M. Abalo & Mahtab Avijgan & Linda Kvastad & Alexandre Denadai-Souza & Nathalie Stakenborg & Ale, 2023. "Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    18. Michaela Schwaiger-Haber & Ethan Stancliffe & Dhanalakshmi S. Anbukumar & Blake Sells & Jia Yi & Kevin Cho & Kayla Adkins-Travis & Milan G. Chheda & Leah P. Shriver & Gary J. Patti, 2023. "Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    19. Zhaohui Cao & Wenlong Zuo & Lanxiang Wang & Junyu Chen & Zepeng Qu & Fan Jin & Lei Dai, 2023. "Spatial profiling of microbial communities by sequential FISH with error-robust encoding," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    20. Xiaomeng Wan & Jiashun Xiao & Sindy Sing Ting Tam & Mingxuan Cai & Ryohichi Sugimura & Yang Wang & Xiang Wan & Zhixiang Lin & Angela Ruohao Wu & Can Yang, 2023. "Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope," Nature Communications, Nature, vol. 14(1), pages 1-22, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37111-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.